Pairwise Similarity Propagation Based Graph Clustering for Scalable Object Indexing and Retrieval

2009 ◽  
pp. 184-194 ◽  
Author(s):  
Shengping Xia ◽  
Edwin R. Hancock
Keyword(s):  
Graph Clustering ◽  
Pairwise Similarity ◽  
Indexing And Retrieval ◽  
Object Indexing ◽  
Similarity Propagation

scholarly journals A NEW APPROACH TO SHAPE-BASED IMAGE RETRIEVAL

2014 ◽  
pp. 99-106
Author(s):  
Mehdi Chehel Amirani ◽  
Zahra Sadeghi Gol ◽  
Ali Asghar Beheshti Shirazi
Keyword(s):  
Image Retrieval ◽  
Curvature Function ◽  
Feature Points ◽  
Retrieval Performance ◽  
New Approach ◽  
Large Databases ◽  
Indexing And Retrieval ◽  
Active Research ◽  
Low Dimensional ◽  
Object Indexing

Content-based image retrieval (CBIR) is very active research topic in recent years. This paper introduces a new approach to shape-based image retrieval. At first, feature points are determined at the boundary of the shape as the extremums of a new version of the curvature function and the initial features are calculated at these points. The proposed method utilizes a supervised system for nonlinear combination of initial features for extraction of efficient and low dimensional feature vector for each shape. The retrieval performance of the approach is illustrated using the MPEG-7 shape database. Our experiments show that the proposed method is well suited for object indexing and retrieval in large databases.

A Graph Clustering Algorithm for the Homology Detection

2011 ◽  
Vol 52-54 ◽  
pp. 1981-1986
Author(s):  
Li Xiao ◽  
Jing Zhong Xiao
Keyword(s):  
Detection Rate ◽  
Clustering Algorithm ◽  
Graph Clustering ◽  
Detection Algorithm ◽  
Homology Detection ◽  
Pairwise Similarity ◽  
Second Stage ◽  
Source Program ◽  
Simulation Results ◽  
Two Phases

In order to detect a large number of source program samples which are homologous files (files with plagiarism), a new graph-based cluster detection algorithm is proposed，the algorithm is divided into two phases, in the first phase, proposed algorithm based on the keyword program to calculate pairwise similarity in the detected sample program files,in the second stage,by means of graph clustering algorithm, the results of the first phase is dectected, homologous files (files with plagiarism) will form a cluster. The simulation results shows that the algorithm improved detection rate compare with the traditional homologous files detection algorithm and can determine which files are homologous.

An Efficient Method for Attributed Graph Clustering

2014 ◽  
Vol 36 (8) ◽  
pp. 1704-1713 ◽  
Author(s):  
Ye WU ◽  
Zhi-Nong ZHONG ◽  
Wei XIONG ◽  
Luo CHEN ◽  
Ning JING
Keyword(s):  
Efficient Method ◽  
Graph Clustering ◽  
Attributed Graph
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